Research News

Oscar of Medical Imaging Goes to Stanford Scientist

A Stanford image combining positron emission tomography (PET) and computed tomography (CT) scans of a patient with lung cancer was chosen from thousands of submissions as the Image of the Year at the annual meeting of the Society of Nuclear Medicine in June.

While combined PET-CT imaging has been around for years, the Stanford image marks the first time the two modalities have been fused into an interactive, three-dimensional format offering both biochemical and structural patient data. Previously it has only been possible to create 3-D visualizations from CT images alone.

Created by assistant professor of radiology Andrew Quon, MD, the combined 3-D image of the patient’s lungs pinpoints the precise location of two malignant lymph nodes, indicating the spread of cancer outside of the patient’s lungs.

By using PET imaging to track the consumption of radioactive glucose molecules that had been injected into the patient, Quon was able to identify regions of high metabolic activity, a common indicator of cancerous growth. After reassembling CT scans of the patient’s chest cavity into a detailed 3-D representation, Quon in turn situated the malignant nodes in the context of the patient’s lungs by fusing the two sets of imaging data into one 3-D model.

Doctors then used a “virtual endoscopy” tool to interact with this model, exploring the patient’s lung region non-surgically. By doing so, they were able to rule out the presence of cancer in the patient’s left main stem bronchus. The close proximity of this bronchus to one of the malignant nodes made it difficult to distinguish and evaluate using only combined 2-D imaging. The 3-D model also helped guide doctors as they performed a surgical biopsy of the nodes.

Quon’s work is part of a larger study by Stanford's Molecular Imaging Program to assess the feasibility of combined 3-D imaging. To facilitate this work, the school’s 3-D medical imaging laboratory developed a new image processing program that applies current algorithms for rendering CT scans in 3-D to PET images.

Preliminary findings show that in some instances, placing biochemical patient data within a detailed, 3-D representation of the patient’s anatomy offers significant clinical advantages over standard 2-D PET/CT imaging during pre-procedural or pre-surgical planning.